{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>create_time</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女</td>\n",
       "      <td>36</td>\n",
       "      <td>1984-10-17</td>\n",
       "      <td>2021-08-25 08:47:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>女</td>\n",
       "      <td>39</td>\n",
       "      <td>1982-10-02</td>\n",
       "      <td>2022-08-14 21:42:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>女</td>\n",
       "      <td>15</td>\n",
       "      <td>2005-10-07</td>\n",
       "      <td>2021-01-23 22:37:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>男</td>\n",
       "      <td>26</td>\n",
       "      <td>1993-08-27</td>\n",
       "      <td>2020-08-20 06:08:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>男</td>\n",
       "      <td>50</td>\n",
       "      <td>1971-02-28</td>\n",
       "      <td>2021-03-26 11:06:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  age  birthdate         create_time  death\n",
       "0      女   36 1984-10-17 2021-08-25 08:47:00      0\n",
       "1      女   39 1982-10-02 2022-08-14 21:42:00      0\n",
       "2      女   15 2005-10-07 2021-01-23 22:37:00      0\n",
       "3      男   26 1993-08-27 2020-08-20 06:08:00      0\n",
       "4      男   50 1971-02-28 2021-03-26 11:06:00      0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#设置最多显示10行\n",
    "pd.set_option('max_rows',10)\n",
    "df = pd.read_csv('iris.csv',encoding='ANSI',engine='python')\n",
    "df['birthdate'] = df['birthdate'].astype('datetime64')\n",
    "df['create_time'] = df['create_time'].astype('datetime64')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['女', '男', '未知'], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看唯一值\n",
    "df.gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36, 39, 15, 26, 50, 61, 13, 45, 59, 23, 31,  7, 43, 11, 34, 57, 73,\n",
       "       54, 35, 48, 27, 38, 21, 14, 17, 22, 84, 28,  8, 58, 64,  5, 30, 24,\n",
       "       12, 44, 20, 80], dtype=int64)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除包含缺失值的行\n",
    "dataset = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>create_time</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender    age  birthdate  create_time  death\n",
       "0    False  False      False        False  False\n",
       "1    False  False      False        False  False\n",
       "2    False  False      False        False  False\n",
       "3    False  False      False        False  False\n",
       "4    False  False      False        False  False\n",
       "..     ...    ...        ...          ...    ...\n",
       "45   False  False      False        False  False\n",
       "46   False  False      False        False  False\n",
       "47   False  False      False        False  False\n",
       "48   False  False      False        False  False\n",
       "49   False  False      False        False  False\n",
       "\n",
       "[50 rows x 5 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将数据集拆分为训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(np.array([df['age']]).reshape(-1, 1),np.array([df['death']]).reshape(-1, 1) , test_size=0.25, random_state=0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86178\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# 特征缩放\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练集的分类器拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(criterion = 'entropy' , random_state=0)\n",
    "#这里的ciass_weight= {0:1,1:10} , max_dep th=20参数可以不用加入\n",
    "# classifier = DecisionTreeClassifier (criterion = 'entropy', random_state=O)\n",
    "classifier.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting the Test set results\n",
    "### 预测测试集结果\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#制作混淆矩阵\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "scoretest =classifier.score(X_test,y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# InvocationException: GraphViz's executables not found 解决方案\n",
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Users/86178/Downloads/stable_windows_10_msbuild_Release_Win32_graphviz-2.49.0-win32/Graphviz/bin/'  #注意修改你的路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from IPython.display import Image\n",
    "import pydotplus\n",
    "from sklearn import tree\n",
    "# 可视化决策树\n",
    "dot_data = tree.export_graphviz(classifier, out_file=None,\n",
    "filled=True, rounded=True, special_characters=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "# 显示图片\n",
    "#graph.get_nodes()[7].set_fillcolor (\"#FFF2DD\")\n",
    "graph.write_png(\"C:/Users/86178/Desktop/out.png\")\n",
    "#这里我把图片输出到桌面的out.png\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "image/png": {
       "height": 2000,
       "width": 2000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='C:/Users/86178/Desktop/out.png', width=2000, height=2000)\n",
    "# 双击图片变大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
